Peritoneal Dialysis–Associated Peritonitis: Suggestions for Management and Mistakes to Avoid
Bibliographic record
Abstract
Peritonitis is a common complication of peritoneal dialysis that is associated with substantial morbidity and mortality. Peritonitis increases treatment costs and hospitalization events and is the most common reason for transfer to hemodialysis. Although there is much focus on preventing peritoneal dialysis-associated peritonitis, equally as important is appropriate management to minimize the morbidity of a peritonitis episode when it has occurred. Despite the presence of international guidelines on peritonitis treatment, the evidence base to support optimal peritonitis treatment practices is lacking, leaving the practitioner to rely on clinical experience and extrapolate from across other infection treatment practices. This article reviews common mistakes and misconceptions that we have observed in the management of peritonitis that may compromise treatment success. It also provides suggestions on common controversial aspects of peritonitis management based on the best available literature. Although the use of the word mistakes is somewhat controversial and subjective, we acknowledge that evidence is lacking and have based many of our suggestions on clinical judgment, experience, and available data.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".